Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
# data_dir = '/input'


# """
# DON'T MODIFY ANYTHING IN THIS CELL
# """
import helper

# helper.download_extract('mnist', data_dir)
# helper.download_extract('celeba', data_dir)

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f5a423af208>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f5a422deda0>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    z_input = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
    learning_rate = tf.placeholder(tf.float32, (None), name='lr')
    # TODO: Implement Function

    return real_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
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==================================
Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(image_batch, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        #input (none, 28, 28, 3)
        x = tf.layers.conv2d(image_batch, filters=64, kernel_size=5, strides=2, padding='same')
        x = tf.maximum(x*alpha, x)
                
        # input (none, 14, 14, 128)
        x = tf.layers.conv2d(x, filters=128, kernel_size=5, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(x*alpha, x)
        
        x = tf.layers.conv2d(x, filters=256, kernel_size=5, strides=1, padding='same')
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(x*alpha, x)
        
        # input 7,7 256
        x = tf.reshape(x, (-1, 7*7*256))
        logits = tf.layers.dense(x, 1)
        out = tf.sigmoid(logits)
            
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, reuse=True, is_train=True, alpha=0.2, test=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    if test:
        reuse = not reuse
    else:
        reuse = False

    with tf.variable_scope('generator', reuse=reuse):
        x = tf.layers.dense(z, 7*7*512)
        x = tf.reshape(x, (-1,7,7,512))
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha*x, x)
        
        x = tf.layers.conv2d_transpose(x, filters=256, kernel_size=5, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(x*alpha, x)
        
        # Shape from 7x7x256 to 14x14x128
        x = tf.layers.conv2d_transpose(x, filters=128, kernel_size=5, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(x*alpha, x)
        
        # Shape from 14x14x128 to 28x28x256
        logits = tf.layers.conv2d_transpose(x, filters=out_channel_dim, kernel_size=5, strides=1, padding='same')
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, test=False)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode,
         print_every = 10, show_every=100):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model   
    real_input, z_input, lr = model_inputs(*data_shape[1:], z_dim)
    d_loss, g_loss = model_loss(real_input, z_input, data_shape[-1])
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    steps = 0
    losses = []
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                steps += 1

                print(steps, end='.')
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                _ = sess.run(d_opt, feed_dict={real_input: batch_images, z_input: batch_z,
                                              lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={real_input: batch_images, z_input: batch_z,
                                              lr: learning_rate})

                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({z_input: batch_z, real_input: batch_images})
                    train_loss_g = g_loss.eval({z_input: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
                    
                if steps % show_every == 0:
                    print(z_input)
                    show_generator_output(sess, 10, z_input, data_shape[-1], data_image_mode)

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 100
z_dim = 80
learning_rate = 0.0004
beta1 = 0.50


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
print("mnist loaded. starting training...")
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

mnist loaded. starting training...
1.2.3.4.5.6.7.8.9.10.Epoch 1/5... Discriminator Loss: 0.6186... Generator Loss: 25.4588
11.12.13.14.15.16.17.18.19.20.Epoch 1/5... Discriminator Loss: 2.2183... Generator Loss: 1.2193
21.22.23.24.25.26.27.28.29.30.Epoch 1/5... Discriminator Loss: 0.0358... Generator Loss: 4.0295
31.32.33.34.35.36.37.38.39.40.Epoch 1/5... Discriminator Loss: 1.9111... Generator Loss: 1.4542
41.42.43.44.45.46.47.48.49.50.Epoch 1/5... Discriminator Loss: 0.4747... Generator Loss: 2.1700
51.52.53.54.55.56.57.58.59.60.Epoch 1/5... Discriminator Loss: 0.1311... Generator Loss: 3.2062
61.62.63.64.65.66.67.68.69.70.Epoch 1/5... Discriminator Loss: 9.0998... Generator Loss: 11.6018
71.72.73.74.75.76.77.78.79.80.Epoch 1/5... Discriminator Loss: 0.9448... Generator Loss: 1.9760
81.82.83.84.85.86.87.88.89.90.Epoch 1/5... Discriminator Loss: 1.6744... Generator Loss: 5.2002
91.92.93.94.95.96.97.98.99.100.Epoch 1/5... Discriminator Loss: 0.3385... Generator Loss: 2.0970
Tensor("z_input:0", shape=(?, 80), dtype=float32)

101.102.103.104.105.106.107.108.109.110.Epoch 1/5... Discriminator Loss: 0.2718... Generator Loss: 2.6244
111.112.113.114.115.116.117.118.119.120.Epoch 1/5... Discriminator Loss: 0.2031... Generator Loss: 3.1260
121.122.123.124.125.126.127.128.129.130.Epoch 1/5... Discriminator Loss: 0.4135... Generator Loss: 2.1800
131.132.133.134.135.136.137.138.139.140.Epoch 1/5... Discriminator Loss: 0.1481... Generator Loss: 2.9100
141.142.143.144.145.146.147.148.149.150.Epoch 1/5... Discriminator Loss: 0.3473... Generator Loss: 1.4975
151.152.153.154.155.156.157.158.159.160.Epoch 1/5... Discriminator Loss: 0.1149... Generator Loss: 3.4685
161.162.163.164.165.166.167.168.169.170.Epoch 1/5... Discriminator Loss: 0.1300... Generator Loss: 2.7318
171.172.173.174.175.176.177.178.179.180.Epoch 1/5... Discriminator Loss: 0.0822... Generator Loss: 3.2838
181.182.183.184.185.186.187.188.189.190.Epoch 1/5... Discriminator Loss: 0.0516... Generator Loss: 3.7507
191.192.193.194.195.196.197.198.199.200.Epoch 1/5... Discriminator Loss: 0.0829... Generator Loss: 3.0583
Tensor("z_input:0", shape=(?, 80), dtype=float32)

201.202.203.204.205.206.207.208.209.210.Epoch 1/5... Discriminator Loss: 2.6607... Generator Loss: 12.7764
211.212.213.214.215.216.217.218.219.220.Epoch 1/5... Discriminator Loss: 0.0530... Generator Loss: 4.8824
221.222.223.224.225.226.227.228.229.230.Epoch 1/5... Discriminator Loss: 0.0828... Generator Loss: 3.4233
231.232.233.234.235.236.237.238.239.240.Epoch 1/5... Discriminator Loss: 0.0710... Generator Loss: 4.8922
241.242.243.244.245.246.247.248.249.250.Epoch 1/5... Discriminator Loss: 0.0089... Generator Loss: 10.3882
251.252.253.254.255.256.257.258.259.260.Epoch 1/5... Discriminator Loss: 0.0846... Generator Loss: 4.4462
261.262.263.264.265.266.267.268.269.270.Epoch 1/5... Discriminator Loss: 0.2046... Generator Loss: 2.8284
271.272.273.274.275.276.277.278.279.280.Epoch 1/5... Discriminator Loss: 0.0352... Generator Loss: 5.0174
281.282.283.284.285.286.287.288.289.290.Epoch 1/5... Discriminator Loss: 0.0235... Generator Loss: 5.1165
291.292.293.294.295.296.297.298.299.300.Epoch 1/5... Discriminator Loss: 0.0923... Generator Loss: 7.3725
Tensor("z_input:0", shape=(?, 80), dtype=float32)

301.302.303.304.305.306.307.308.309.310.Epoch 1/5... Discriminator Loss: 0.2049... Generator Loss: 4.0770
311.312.313.314.315.316.317.318.319.320.Epoch 1/5... Discriminator Loss: 0.0409... Generator Loss: 4.9893
321.322.323.324.325.326.327.328.329.330.Epoch 1/5... Discriminator Loss: 0.1048... Generator Loss: 8.7449
331.332.333.334.335.336.337.338.339.340.Epoch 1/5... Discriminator Loss: 0.1818... Generator Loss: 2.6118
341.342.343.344.345.346.347.348.349.350.Epoch 1/5... Discriminator Loss: 0.0224... Generator Loss: 5.2190
351.352.353.354.355.356.357.358.359.360.Epoch 1/5... Discriminator Loss: 0.0100... Generator Loss: 6.9532
361.362.363.364.365.366.367.368.369.370.Epoch 1/5... Discriminator Loss: 0.0287... Generator Loss: 7.0792
371.372.373.374.375.376.377.378.379.380.Epoch 1/5... Discriminator Loss: 0.0546... Generator Loss: 3.5280
381.382.383.384.385.386.387.388.389.390.Epoch 1/5... Discriminator Loss: 0.3246... Generator Loss: 4.6904
391.392.393.394.395.396.397.398.399.400.Epoch 1/5... Discriminator Loss: 0.0792... Generator Loss: 4.8619
Tensor("z_input:0", shape=(?, 80), dtype=float32)

401.402.403.404.405.406.407.408.409.410.Epoch 1/5... Discriminator Loss: 0.0693... Generator Loss: 3.9769
411.412.413.414.415.416.417.418.419.420.Epoch 1/5... Discriminator Loss: 0.0173... Generator Loss: 4.9734
421.422.423.424.425.426.427.428.429.430.Epoch 1/5... Discriminator Loss: 0.1057... Generator Loss: 4.3114
431.432.433.434.435.436.437.438.439.440.Epoch 1/5... Discriminator Loss: 0.1214... Generator Loss: 3.4764
441.442.443.444.445.446.447.448.449.450.Epoch 1/5... Discriminator Loss: 0.0492... Generator Loss: 4.3376
451.452.453.454.455.456.457.458.459.460.Epoch 1/5... Discriminator Loss: 0.0280... Generator Loss: 5.0053
461.462.463.464.465.466.467.468.469.470.Epoch 1/5... Discriminator Loss: 3.0612... Generator Loss: 0.1057
471.472.473.474.475.476.477.478.479.480.Epoch 1/5... Discriminator Loss: 2.3674... Generator Loss: 5.7972
481.482.483.484.485.486.487.488.489.490.Epoch 1/5... Discriminator Loss: 0.6815... Generator Loss: 6.1609
491.492.493.494.495.496.497.498.499.500.Epoch 1/5... Discriminator Loss: 0.1222... Generator Loss: 5.0414
Tensor("z_input:0", shape=(?, 80), dtype=float32)

501.502.503.504.505.506.507.508.509.510.Epoch 1/5... Discriminator Loss: 0.1988... Generator Loss: 5.2159
511.512.513.514.515.516.517.518.519.520.Epoch 1/5... Discriminator Loss: 0.5672... Generator Loss: 7.8378
521.522.523.524.525.526.527.528.529.530.Epoch 1/5... Discriminator Loss: 1.5313... Generator Loss: 0.4200
531.532.533.534.535.536.537.538.539.540.Epoch 1/5... Discriminator Loss: 0.7351... Generator Loss: 1.2763
541.542.543.544.545.546.547.548.549.550.Epoch 1/5... Discriminator Loss: 0.1487... Generator Loss: 3.3440
551.552.553.554.555.556.557.558.559.560.Epoch 1/5... Discriminator Loss: 0.2047... Generator Loss: 3.6504
561.562.563.564.565.566.567.568.569.570.Epoch 1/5... Discriminator Loss: 0.2263... Generator Loss: 2.7692
571.572.573.574.575.576.577.578.579.580.Epoch 1/5... Discriminator Loss: 0.5783... Generator Loss: 4.3417
581.582.583.584.585.586.587.588.589.590.Epoch 1/5... Discriminator Loss: 1.4256... Generator Loss: 0.4743
591.592.593.594.595.596.597.598.599.600.Epoch 1/5... Discriminator Loss: 0.2643... Generator Loss: 3.5550
Tensor("z_input:0", shape=(?, 80), dtype=float32)

601.602.603.604.605.606.607.608.609.610.Epoch 2/5... Discriminator Loss: 0.0809... Generator Loss: 4.0803
611.612.613.614.615.616.617.618.619.620.Epoch 2/5... Discriminator Loss: 0.4749... Generator Loss: 9.3318
621.622.623.624.625.626.627.628.629.630.Epoch 2/5... Discriminator Loss: 0.0089... Generator Loss: 6.2410
631.632.633.634.635.636.637.638.639.640.Epoch 2/5... Discriminator Loss: 0.0316... Generator Loss: 5.0310
641.642.643.644.645.646.647.648.649.650.Epoch 2/5... Discriminator Loss: 0.0217... Generator Loss: 5.7480
651.652.653.654.655.656.657.658.659.660.Epoch 2/5... Discriminator Loss: 0.2348... Generator Loss: 2.7185
661.662.663.664.665.666.667.668.669.670.Epoch 2/5... Discriminator Loss: 0.0120... Generator Loss: 5.4132
671.672.673.674.675.676.677.678.679.680.Epoch 2/5... Discriminator Loss: 0.0267... Generator Loss: 4.8329
681.682.683.684.685.686.687.688.689.690.Epoch 2/5... Discriminator Loss: 0.0175... Generator Loss: 6.0626
691.692.693.694.695.696.697.698.699.700.Epoch 2/5... Discriminator Loss: 0.0112... Generator Loss: 6.3440
Tensor("z_input:0", shape=(?, 80), dtype=float32)

701.702.703.704.705.706.707.708.709.710.Epoch 2/5... Discriminator Loss: 0.0289... Generator Loss: 8.6301
711.712.713.714.715.716.717.718.719.720.Epoch 2/5... Discriminator Loss: 0.0702... Generator Loss: 3.8561
721.722.723.724.725.726.727.728.729.730.Epoch 2/5... Discriminator Loss: 0.0153... Generator Loss: 5.5295
731.732.733.734.735.736.737.738.739.740.Epoch 2/5... Discriminator Loss: 0.0125... Generator Loss: 5.8203
741.742.743.744.745.746.747.748.749.750.Epoch 2/5... Discriminator Loss: 0.0266... Generator Loss: 4.6140
751.752.753.754.755.756.757.758.759.760.Epoch 2/5... Discriminator Loss: 0.0115... Generator Loss: 5.5917
761.762.763.764.765.766.767.768.769.770.Epoch 2/5... Discriminator Loss: 0.0070... Generator Loss: 5.8082
771.772.773.774.775.776.777.778.779.780.Epoch 2/5... Discriminator Loss: 0.0276... Generator Loss: 5.4277
781.782.783.784.785.786.787.788.789.790.Epoch 2/5... Discriminator Loss: 0.0045... Generator Loss: 7.4325
791.792.793.794.795.796.797.798.799.800.Epoch 2/5... Discriminator Loss: 0.1261... Generator Loss: 3.0171
Tensor("z_input:0", shape=(?, 80), dtype=float32)

801.802.803.804.805.806.807.808.809.810.Epoch 2/5... Discriminator Loss: 0.0072... Generator Loss: 8.1162
811.812.813.814.815.816.817.818.819.820.Epoch 2/5... Discriminator Loss: 0.0096... Generator Loss: 8.0482
821.822.823.824.825.826.827.828.829.830.Epoch 2/5... Discriminator Loss: 0.0589... Generator Loss: 3.7587
831.832.833.834.835.836.837.838.839.840.Epoch 2/5... Discriminator Loss: 0.0037... Generator Loss: 9.2112
841.842.843.844.845.846.847.848.849.850.Epoch 2/5... Discriminator Loss: 0.0119... Generator Loss: 7.5012
851.852.853.854.855.856.857.858.859.860.Epoch 2/5... Discriminator Loss: 0.0427... Generator Loss: 4.0501
861.862.863.864.865.866.867.868.869.870.Epoch 2/5... Discriminator Loss: 0.0414... Generator Loss: 4.8055
871.872.873.874.875.876.877.878.879.880.Epoch 2/5... Discriminator Loss: 0.0335... Generator Loss: 4.9710
881.882.883.884.885.886.887.888.889.890.Epoch 2/5... Discriminator Loss: 0.0130... Generator Loss: 6.8232
891.892.893.894.895.896.897.898.899.900.Epoch 2/5... Discriminator Loss: 0.0095... Generator Loss: 7.1291
Tensor("z_input:0", shape=(?, 80), dtype=float32)

901.902.903.904.905.906.907.908.909.910.Epoch 2/5... Discriminator Loss: 0.0241... Generator Loss: 4.9730
911.912.913.914.915.916.917.918.919.920.Epoch 2/5... Discriminator Loss: 0.0136... Generator Loss: 5.4602
921.922.923.924.925.926.927.928.929.930.Epoch 2/5... Discriminator Loss: 0.0232... Generator Loss: 5.4214
931.932.933.934.935.936.937.938.939.940.Epoch 2/5... Discriminator Loss: 0.0342... Generator Loss: 8.2735
941.942.943.944.945.946.947.948.949.950.Epoch 2/5... Discriminator Loss: 9.0764... Generator Loss: 0.0108
951.952.953.954.955.956.957.958.959.960.Epoch 2/5... Discriminator Loss: 1.2572... Generator Loss: 1.7836
961.962.963.964.965.966.967.968.969.970.Epoch 2/5... Discriminator Loss: 1.0333... Generator Loss: 0.6703
971.972.973.974.975.976.977.978.979.980.Epoch 2/5... Discriminator Loss: 1.5033... Generator Loss: 0.5613
981.982.983.984.985.986.987.988.989.990.Epoch 2/5... Discriminator Loss: 1.4755... Generator Loss: 1.0089
991.992.993.994.995.996.997.998.999.1000.Epoch 2/5... Discriminator Loss: 0.6446... Generator Loss: 1.1356
Tensor("z_input:0", shape=(?, 80), dtype=float32)

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 32
z_dim = 80
learning_rate = 0.0004
beta1 = 0.50


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

1.2.3.4.5.6.7.8.9.10.Epoch 1/5... Discriminator Loss: 0.0957... Generator Loss: 9.9209
11.12.13.14.15.16.17.18.19.20.Epoch 1/5... Discriminator Loss: 0.0260... Generator Loss: 14.0094
21.22.23.24.25.26.27.28.29.30.Epoch 1/5... Discriminator Loss: 0.0643... Generator Loss: 4.0951
31.32.33.34.35.36.37.38.39.40.Epoch 1/5... Discriminator Loss: 0.8348... Generator Loss: 7.4560
41.42.43.44.45.46.47.48.49.50.Epoch 1/5... Discriminator Loss: 0.4149... Generator Loss: 3.6086
51.52.53.54.55.56.57.58.59.60.Epoch 1/5... Discriminator Loss: 0.6760... Generator Loss: 1.2192
61.62.63.64.65.66.67.68.69.70.Epoch 1/5... Discriminator Loss: 0.7383... Generator Loss: 0.9999
71.72.73.74.75.76.77.78.79.80.Epoch 1/5... Discriminator Loss: 0.1411... Generator Loss: 3.5204
81.82.83.84.85.86.87.88.89.90.Epoch 1/5... Discriminator Loss: 0.0223... Generator Loss: 4.6589
91.92.93.94.95.96.97.98.99.100.Epoch 1/5... Discriminator Loss: 0.0063... Generator Loss: 6.3514
Tensor("z_input:0", shape=(?, 80), dtype=float32)

101.102.103.104.105.106.107.108.109.110.Epoch 1/5... Discriminator Loss: 0.0068... Generator Loss: 6.4306
111.112.113.114.115.116.117.118.119.120.Epoch 1/5... Discriminator Loss: 0.0124... Generator Loss: 4.9298
121.122.123.124.125.126.127.128.129.130.Epoch 1/5... Discriminator Loss: 0.0134... Generator Loss: 4.4927
131.132.133.134.135.136.137.138.139.140.Epoch 1/5... Discriminator Loss: 2.8542... Generator Loss: 20.5592
141.142.143.144.145.146.147.148.149.150.Epoch 1/5... Discriminator Loss: 1.8144... Generator Loss: 1.6059
151.152.153.154.155.156.157.158.159.160.Epoch 1/5... Discriminator Loss: 0.4573... Generator Loss: 2.2182
161.162.163.164.165.166.167.168.169.170.Epoch 1/5... Discriminator Loss: 0.0006... Generator Loss: 9.1271
171.172.173.174.175.176.177.178.179.180.Epoch 1/5... Discriminator Loss: 0.5181... Generator Loss: 1.9631
181.182.183.184.185.186.187.188.189.190.Epoch 1/5... Discriminator Loss: 0.1475... Generator Loss: 3.3917
191.192.193.194.195.196.197.198.199.200.Epoch 1/5... Discriminator Loss: 0.0826... Generator Loss: 3.1226
Tensor("z_input:0", shape=(?, 80), dtype=float32)

201.202.203.204.205.206.207.208.209.210.Epoch 1/5... Discriminator Loss: 0.1326... Generator Loss: 2.5765
211.212.213.214.215.216.217.218.219.220.Epoch 1/5... Discriminator Loss: 0.0331... Generator Loss: 4.1165
221.222.223.224.225.226.227.228.229.230.Epoch 1/5... Discriminator Loss: 1.0822... Generator Loss: 1.7091
231.232.233.234.235.236.237.238.239.240.Epoch 1/5... Discriminator Loss: 0.0696... Generator Loss: 3.5929
241.242.243.244.245.246.247.248.249.250.Epoch 1/5... Discriminator Loss: 1.7239... Generator Loss: 1.7742
251.252.253.254.255.256.257.258.259.260.Epoch 1/5... Discriminator Loss: 0.3165... Generator Loss: 1.8098
261.262.263.264.265.266.267.268.269.270.Epoch 1/5... Discriminator Loss: 0.9902... Generator Loss: 0.6060
271.272.273.274.275.276.277.278.279.280.Epoch 1/5... Discriminator Loss: 3.0736... Generator Loss: 0.1058
281.282.283.284.285.286.287.288.289.290.Epoch 1/5... Discriminator Loss: 1.8361... Generator Loss: 0.5512
291.292.293.294.295.296.297.298.299.300.Epoch 1/5... Discriminator Loss: 2.3237... Generator Loss: 3.5224
Tensor("z_input:0", shape=(?, 80), dtype=float32)

301.302.303.304.305.306.307.308.309.310.Epoch 1/5... Discriminator Loss: 0.7218... Generator Loss: 1.2559
311.312.313.314.315.316.317.318.319.320.Epoch 1/5... Discriminator Loss: 0.3505... Generator Loss: 1.9586
321.322.323.324.325.326.327.328.329.330.Epoch 1/5... Discriminator Loss: 0.0603... Generator Loss: 6.8343
331.332.333.334.335.336.337.338.339.340.Epoch 1/5... Discriminator Loss: 0.3514... Generator Loss: 1.6626
341.342.343.344.345.346.347.348.349.350.Epoch 1/5... Discriminator Loss: 0.1011... Generator Loss: 4.3478
351.352.353.354.355.356.357.358.359.360.Epoch 1/5... Discriminator Loss: 3.0566... Generator Loss: 0.1006
361.362.363.364.365.366.367.368.369.370.Epoch 1/5... Discriminator Loss: 0.1502... Generator Loss: 2.7355
371.372.373.374.375.376.377.378.379.380.Epoch 1/5... Discriminator Loss: 0.1075... Generator Loss: 4.5985
381.382.383.384.385.386.387.388.389.390.Epoch 1/5... Discriminator Loss: 0.4727... Generator Loss: 1.8254
391.392.393.394.395.396.397.398.399.400.Epoch 1/5... Discriminator Loss: 0.0380... Generator Loss: 4.7752
Tensor("z_input:0", shape=(?, 80), dtype=float32)

401.402.403.404.405.406.407.408.409.410.Epoch 1/5... Discriminator Loss: 0.2638... Generator Loss: 2.3857
411.412.413.414.415.416.417.418.419.420.Epoch 1/5... Discriminator Loss: 0.0331... Generator Loss: 6.5269
421.422.423.424.425.426.427.428.429.430.Epoch 1/5... Discriminator Loss: 0.2295... Generator Loss: 5.2599
431.432.433.434.435.436.437.438.439.440.Epoch 1/5... Discriminator Loss: 0.0288... Generator Loss: 5.5278
441.442.443.444.445.446.447.448.449.450.Epoch 1/5... Discriminator Loss: 0.0609... Generator Loss: 4.4844
451.452.453.454.455.456.457.458.459.460.Epoch 1/5... Discriminator Loss: 0.6305... Generator Loss: 8.3832
461.462.463.464.465.466.467.468.469.470.Epoch 1/5... Discriminator Loss: 0.2134... Generator Loss: 3.2350
471.472.473.474.475.476.477.478.479.480.Epoch 1/5... Discriminator Loss: 0.0776... Generator Loss: 5.1660
481.482.483.484.485.486.487.488.489.490.Epoch 1/5... Discriminator Loss: 0.0125... Generator Loss: 5.9202
491.492.493.494.495.496.497.498.499.500.Epoch 1/5... Discriminator Loss: 0.0383... Generator Loss: 5.0548
Tensor("z_input:0", shape=(?, 80), dtype=float32)

501.502.503.504.505.506.507.508.509.510.Epoch 1/5... Discriminator Loss: 0.1048... Generator Loss: 2.9320
511.512.513.514.515.516.517.518.519.520.Epoch 1/5... Discriminator Loss: 0.2978... Generator Loss: 2.5678
521.522.523.524.525.526.527.528.529.530.Epoch 1/5... Discriminator Loss: 0.1035... Generator Loss: 7.3994
531.532.533.534.535.536.537.538.539.540.Epoch 1/5... Discriminator Loss: 0.0586... Generator Loss: 4.1373
541.542.543.544.545.546.547.548.549.550.Epoch 1/5... Discriminator Loss: 0.5625... Generator Loss: 1.6001
551.552.553.554.555.556.557.558.559.560.Epoch 1/5... Discriminator Loss: 0.1175... Generator Loss: 3.3748
561.562.563.564.565.566.567.568.569.570.Epoch 1/5... Discriminator Loss: 0.1339... Generator Loss: 4.3378
571.572.573.574.575.576.577.578.579.580.Epoch 1/5... Discriminator Loss: 0.0453... Generator Loss: 6.0222
581.582.583.584.585.586.587.588.589.590.Epoch 1/5... Discriminator Loss: 0.1030... Generator Loss: 3.7412
591.592.593.594.595.596.597.598.599.600.Epoch 1/5... Discriminator Loss: 7.9530... Generator Loss: 0.0144
Tensor("z_input:0", shape=(?, 80), dtype=float32)

601.602.603.604.605.606.607.608.609.610.Epoch 1/5... Discriminator Loss: 0.1020... Generator Loss: 3.6354
611.612.613.614.615.616.617.618.619.620.Epoch 1/5... Discriminator Loss: 0.0402... Generator Loss: 5.0308
621.622.623.624.625.626.627.628.629.630.Epoch 1/5... Discriminator Loss: 1.0816... Generator Loss: 3.8530
631.632.633.634.635.636.637.638.639.640.Epoch 1/5... Discriminator Loss: 0.3822... Generator Loss: 1.7819
641.642.643.644.645.646.647.648.649.650.Epoch 1/5... Discriminator Loss: 0.0208... Generator Loss: 6.8354
651.652.653.654.655.656.657.658.659.660.Epoch 1/5... Discriminator Loss: 0.0924... Generator Loss: 3.8862
661.662.663.664.665.666.667.668.669.670.Epoch 1/5... Discriminator Loss: 0.2226... Generator Loss: 3.4732
671.672.673.674.675.676.677.678.679.680.Epoch 1/5... Discriminator Loss: 0.1744... Generator Loss: 2.4315
681.682.683.684.685.686.687.688.689.690.Epoch 1/5... Discriminator Loss: 0.0530... Generator Loss: 4.8979
691.692.693.694.695.696.697.698.699.700.Epoch 1/5... Discriminator Loss: 6.2798... Generator Loss: 11.1003
Tensor("z_input:0", shape=(?, 80), dtype=float32)

701.702.703.704.705.706.707.708.709.710.Epoch 1/5... Discriminator Loss: 0.0322... Generator Loss: 7.0830
711.712.713.714.715.716.717.718.719.720.Epoch 1/5... Discriminator Loss: 0.2299... Generator Loss: 2.6605
721.722.723.724.725.726.727.728.729.730.Epoch 1/5... Discriminator Loss: 0.0854... Generator Loss: 4.4142
731.732.733.734.735.736.737.738.739.740.Epoch 1/5... Discriminator Loss: 0.0150... Generator Loss: 5.2538
741.742.743.744.745.746.747.748.749.750.Epoch 1/5... Discriminator Loss: 0.0265... Generator Loss: 5.0746
751.752.753.754.755.756.757.758.759.760.Epoch 1/5... Discriminator Loss: 0.1579... Generator Loss: 4.3164
761.762.763.764.765.766.767.768.769.770.Epoch 1/5... Discriminator Loss: 0.0110... Generator Loss: 8.2173
771.772.773.774.775.776.777.778.779.780.Epoch 1/5... Discriminator Loss: 2.8755... Generator Loss: 2.3441
781.782.783.784.785.786.787.788.789.790.Epoch 1/5... Discriminator Loss: 0.6094... Generator Loss: 1.2044
791.792.793.794.795.796.797.798.799.800.Epoch 1/5... Discriminator Loss: 0.0676... Generator Loss: 4.7980
Tensor("z_input:0", shape=(?, 80), dtype=float32)

801.802.803.804.805.806.807.808.809.810.Epoch 1/5... Discriminator Loss: 0.0795... Generator Loss: 5.4473
811.812.813.814.815.816.817.818.819.820.Epoch 1/5... Discriminator Loss: 0.3987... Generator Loss: 1.7792
821.822.823.824.825.826.827.828.829.830.Epoch 1/5... Discriminator Loss: 0.1827... Generator Loss: 2.5026
831.832.833.834.835.836.837.838.839.840.Epoch 1/5... Discriminator Loss: 0.0284... Generator Loss: 4.3885
841.842.843.844.845.846.847.848.849.850.Epoch 1/5... Discriminator Loss: 3.3553... Generator Loss: 0.0695
851.852.853.854.855.856.857.858.859.860.Epoch 1/5... Discriminator Loss: 0.6396... Generator Loss: 1.4780
861.862.863.864.865.866.867.868.869.870.Epoch 1/5... Discriminator Loss: 0.0226... Generator Loss: 6.7938
871.872.873.874.875.876.877.878.879.880.Epoch 1/5... Discriminator Loss: 0.0989... Generator Loss: 3.4701
881.882.883.884.885.886.887.888.889.890.Epoch 1/5... Discriminator Loss: 0.0539... Generator Loss: 5.4650
891.892.893.894.895.896.897.898.899.900.Epoch 1/5... Discriminator Loss: 0.0508... Generator Loss: 5.1486
Tensor("z_input:0", shape=(?, 80), dtype=float32)

901.902.903.904.905.906.907.908.909.910.Epoch 1/5... Discriminator Loss: 0.0767... Generator Loss: 3.0521
911.912.913.914.915.916.917.918.919.920.Epoch 1/5... Discriminator Loss: 0.0971... Generator Loss: 3.4065
921.922.923.924.925.926.927.928.929.930.Epoch 1/5... Discriminator Loss: 0.0796... Generator Loss: 3.9856
931.932.933.934.935.936.937.938.939.940.Epoch 1/5... Discriminator Loss: 0.2295... Generator Loss: 3.0876
941.942.943.944.945.946.947.948.949.950.Epoch 1/5... Discriminator Loss: 0.0066... Generator Loss: 7.2379
951.952.953.954.955.956.957.958.959.960.Epoch 1/5... Discriminator Loss: 0.0058... Generator Loss: 7.3534
961.962.963.964.965.966.967.968.969.970.Epoch 1/5... Discriminator Loss: 0.0001... Generator Loss: 14.0237
971.972.973.974.975.976.977.978.979.980.Epoch 1/5... Discriminator Loss: 0.1705... Generator Loss: 2.7290
981.982.983.984.985.986.987.988.989.990.Epoch 1/5... Discriminator Loss: 0.0013... Generator Loss: 8.7740
991.992.993.994.995.996.997.998.999.1000.Epoch 1/5... Discriminator Loss: 0.0112... Generator Loss: 6.2012
Tensor("z_input:0", shape=(?, 80), dtype=float32)

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.